From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Introduction to linear and nonlinear programming

50 Years of Image Analysis

The diffraction tomography theorem is adapted to one-dimensional length measurement. The resulting spectral interferometry technique is described and the first length measurements using this technique on a model eye and on a human eye in vivo are presented.

Measurement of intraocular distances by backscattering spectral interferometry

If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5? The answer is probably a No. Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.

/pdf/age-progression-regression-by-conditional-adversarial-4o0qnp7kc0.pdf

Age Progression/Regression by Conditional Adversarial Autoencoder

We propose a novel joint compressive imaging system, which combines the merit of Single Pixel Camera (SPC) and Coded Aperture Snapshot Spectral Imaging (CASSI) system. This enables us to capture multi- or hyperspectral information with a single pixel detector. The desired 3D image cube is reconstructed by a concatenation of deep-unfolding-based algorithm and plug-and-play algorithm with deep-learning-based denoiser. We demonstrate the feasibility of the proposed system in both simulation and experiments. With advanced algorithms, the joint compressive imaging system is able to output comparable hyperspectral images with existing SD-CASSI system. Moreover, by adapting ultra-broad-spectrum photodiodes, the proposed system can be easily expanded to Near- and Mid-infrared band and thus being a low-cost approach to IR spectroscopy.

Deep-learning enables single-pixel spectral imaging

/pdf/a-mutually-boosting-dual-sensor-computational-camera-for-1fvfemee.pdf

A mutually boosting dual sensor computational camera for high quality dark videography

We introduce VERTEX, an effective solution to recovering the 3D shape and texture of vehicles from uncalibrated monocular inputs under real-world street environments. To fully utilize the semantic prior of vehicles, we propose a novel geometry and texture joint representation based on implicit semantic template mapping. Compared to existing representations which infer 3D texture fields, our method explicitly constrains the texture distribution on the 2D surface of the template and avoids the limitation of fixed topology. Moreover, we propose a joint training strategy that leverages the texture distribution to learn a semantic-preserving mapping from vehicle instances to the canonical template. We also contribute a new synthetic dataset containing 830 elaborately textured car models labeled with key points and rendered using Physically Based Rendering (PBRT) system with measured HDRI skymaps to obtain highly realistic images. Experiments demonstrate the superior performance of our approach on both testing dataset and in-the-wild images. Furthermore, the presented technique enables additional applications such as 3D vehicle texture transfer and material identification, and can be generalized to other shape categories. 

VERTEX: VEhicle Reconstruction and TEXture Estimation from a Single Image Using Deep Implicit Semantic Template Mapping

And-Or Graph Face Model and Its Applications in Artistic Sketching and Aging Simulation.

Image deconvolution appears in many image-related problems. Previous works tried to train neural networks directly on blurry/clean pairs to restore clean images but failed. In this work, we propose a novel neural network, trained end-to-end, pixels-to-pixels, to deblur images from blurry ones. Our key insight is to build multi-scale convolutional neural networks that extract various scale feature maps which is essential for recovering sharp images and removing artifacts. The networks take input image of arbitrary size and produce output within efficient time. We demonstrate that our approach yields better result than the state-of-the-art deconvolution algorithms on a large dataset.

Jinli Suo

Papers

Deep-learning enables single-pixel spectral imaging

A mutually boosting dual sensor computational camera for high quality dark videography

VERTEX: VEhicle Reconstruction and TEXture Estimation from a Single Image Using Deep Implicit Semantic Template Mapping

And-Or Graph Face Model and Its Applications in Artistic Sketching and Aging Simulation.

Multi-scale Convolutional Neural Networks for Non-blind Image Deconvolution